lsst.pipe.base  20.0.0-23-g8900aa8+dfd6ff4ddf
graphBuilder.py
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21 from __future__ import annotations
22 
23 """Module defining GraphBuilder class and related methods.
24 """
25 
26 __all__ = ['GraphBuilder']
27 
28 # -------------------------------
29 # Imports of standard modules --
30 # -------------------------------
31 import itertools
32 from collections import ChainMap
33 from contextlib import contextmanager
34 from dataclasses import dataclass
35 from typing import Dict, Iterable, Iterator, List, Set
36 import logging
37 
38 
39 # -----------------------------
40 # Imports for other modules --
41 # -----------------------------
42 from .connections import iterConnections
43 from .pipeline import PipelineDatasetTypes, TaskDatasetTypes, TaskDef, Pipeline
44 from .graph import QuantumGraph
45 from lsst.daf.butler import (
46  DataCoordinate,
47  DatasetRef,
48  DatasetType,
49  DimensionGraph,
50  DimensionUniverse,
51  NamedKeyDict,
52  Quantum,
53 )
54 from lsst.daf.butler.registry.queries.exprParser import ParseError, ParserYacc, TreeVisitor
55 from lsst.utils import doImport
56 
57 # ----------------------------------
58 # Local non-exported definitions --
59 # ----------------------------------
60 
61 _LOG = logging.getLogger(__name__.partition(".")[2])
62 
63 
64 class _DatasetDict(NamedKeyDict[DatasetType, Dict[DataCoordinate, DatasetRef]]):
65  """A custom dictionary that maps `DatasetType` to a nested dictionary of
66  the known `DatasetRef` instances of that type.
67 
68  Parameters
69  ----------
70  args
71  Positional arguments are forwarded to the `dict` constructor.
72  universe : `DimensionUniverse`
73  Universe of all possible dimensions.
74  """
75  def __init__(self, *args, universe: DimensionGraph):
76  super().__init__(*args)
77  self.universe = universe
78 
79  @classmethod
80  def fromDatasetTypes(cls, datasetTypes: Iterable[DatasetType], *,
81  universe: DimensionUniverse) -> _DatasetDict:
82  """Construct a dictionary from a flat iterable of `DatasetType` keys.
83 
84  Parameters
85  ----------
86  datasetTypes : `iterable` of `DatasetType`
87  DatasetTypes to use as keys for the dict. Values will be empty
88  dictionaries.
89  universe : `DimensionUniverse`
90  Universe of all possible dimensions.
91 
92  Returns
93  -------
94  dictionary : `_DatasetDict`
95  A new `_DatasetDict` instance.
96  """
97  return cls({datasetType: {} for datasetType in datasetTypes}, universe=universe)
98 
99  @classmethod
100  def fromSubset(cls, datasetTypes: Iterable[DatasetType], first: _DatasetDict, *rest: _DatasetDict
101  ) -> _DatasetDict:
102  """Return a new dictionary by extracting items corresponding to the
103  given keys from one or more existing dictionaries.
104 
105  Parameters
106  ----------
107  datasetTypes : `iterable` of `DatasetType`
108  DatasetTypes to use as keys for the dict. Values will be obtained
109  by lookups against ``first`` and ``rest``.
110  first : `_DatasetDict`
111  Another dictionary from which to extract values.
112  rest
113  Additional dictionaries from which to extract values.
114 
115  Returns
116  -------
117  dictionary : `_DatasetDict`
118  A new dictionary instance.
119  """
120  combined = ChainMap(first, *rest)
121  return cls({datasetType: combined[datasetType] for datasetType in datasetTypes},
122  universe=first.universe)
123 
124  @property
125  def dimensions(self) -> DimensionGraph:
126  """The union of all dimensions used by all dataset types in this
127  dictionary, including implied dependencies (`DimensionGraph`).
128  """
129  base = self.universe.empty
130  if len(self) == 0:
131  return base
132  return base.union(*[datasetType.dimensions for datasetType in self.keys()])
133 
134  def unpackSingleRefs(self) -> NamedKeyDict[DatasetType, DatasetRef]:
135  """Unpack nested single-element `DatasetRef` dicts into a new
136  mapping with `DatasetType` keys and `DatasetRef` values.
137 
138  This method assumes that each nest contains exactly one item, as is the
139  case for all "init" datasets.
140 
141  Returns
142  -------
143  dictionary : `NamedKeyDict`
144  Dictionary mapping `DatasetType` to `DatasetRef`, with both
145  `DatasetType` instances and string names usable as keys.
146  """
147  def getOne(refs: Dict[DataCoordinate, DatasetRef]) -> DatasetRef:
148  ref, = refs.values()
149  return ref
150  return NamedKeyDict({datasetType: getOne(refs) for datasetType, refs in self.items()})
151 
152  def unpackMultiRefs(self) -> NamedKeyDict[DatasetType, DatasetRef]:
153  """Unpack nested multi-element `DatasetRef` dicts into a new
154  mapping with `DatasetType` keys and `set` of `DatasetRef` values.
155 
156  Returns
157  -------
158  dictionary : `NamedKeyDict`
159  Dictionary mapping `DatasetType` to `DatasetRef`, with both
160  `DatasetType` instances and string names usable as keys.
161  """
162  return NamedKeyDict({datasetType: list(refs.values()) for datasetType, refs in self.items()})
163 
164  def extract(self, datasetType: DatasetType, dataIds: Iterable[DataCoordinate]
165  ) -> Iterator[DatasetRef]:
166  """Iterate over the contained `DatasetRef` instances that match the
167  given `DatasetType` and data IDs.
168 
169  Parameters
170  ----------
171  datasetType : `DatasetType`
172  Dataset type to match.
173  dataIds : `Iterable` [ `DataCoordinate` ]
174  Data IDs to match.
175 
176  Returns
177  -------
178  refs : `Iterator` [ `DatasetRef` ]
179  DatasetRef instances for which ``ref.datasetType == datasetType``
180  and ``ref.dataId`` is in ``dataIds``.
181  """
182  refs = self[datasetType]
183  return (refs[dataId] for dataId in dataIds)
184 
185 
187  """Helper class aggregating information about a `Quantum`, used when
188  constructing a `QuantumGraph`.
189 
190  See `_PipelineScaffolding` for a top-down description of the full
191  scaffolding data structure.
192 
193  Parameters
194  ----------
195  task : _TaskScaffolding
196  Back-reference to the helper object for the `PipelineTask` this quantum
197  represents an execution of.
198  dataId : `DataCoordinate`
199  Data ID for this quantum.
200  """
201  def __init__(self, task: _TaskScaffolding, dataId: DataCoordinate):
202  self.task = task
203  self.dataId = dataId
204  self.inputs = _DatasetDict.fromDatasetTypes(task.inputs.keys(), universe=dataId.universe)
205  self.outputs = _DatasetDict.fromDatasetTypes(task.outputs.keys(), universe=dataId.universe)
206  self.prerequisites = _DatasetDict.fromDatasetTypes(task.prerequisites.keys(),
207  universe=dataId.universe)
208 
209  __slots__ = ("task", "dataId", "inputs", "outputs", "prerequisites")
210 
211  def __repr__(self):
212  return f"_QuantumScaffolding(taskDef={self.task.taskDef}, dataId={self.dataId}, ...)"
213 
214  task: _TaskScaffolding
215  """Back-reference to the helper object for the `PipelineTask` this quantum
216  represents an execution of.
217  """
218 
219  dataId: DataCoordinate
220  """Data ID for this quantum.
221  """
222 
223  inputs: _DatasetDict
224  """Nested dictionary containing `DatasetRef` inputs to this quantum.
225 
226  This is initialized to map each `DatasetType` to an empty dictionary at
227  construction. Those nested dictionaries are populated (with data IDs as
228  keys) with unresolved `DatasetRef` instances in
229  `_PipelineScaffolding.connectDataIds`.
230  """
231 
232  outputs: _DatasetDict
233  """Nested dictionary containing `DatasetRef` outputs this quantum.
234  """
235 
236  prerequisites: _DatasetDict
237  """Nested dictionary containing `DatasetRef` prerequisite inputs to this
238  quantum.
239  """
240 
241  def makeQuantum(self) -> Quantum:
242  """Transform the scaffolding object into a true `Quantum` instance.
243 
244  Returns
245  -------
246  quantum : `Quantum`
247  An actual `Quantum` instance.
248  """
249  allInputs = self.inputs.unpackMultiRefs()
250  allInputs.update(self.prerequisites.unpackMultiRefs())
251  # Give the task's Connections class an opportunity to remove some
252  # inputs, or complain if they are unacceptable.
253  # This will raise if one of the check conditions is not met, which is
254  # the intended behavior
255  allInputs = self.task.taskDef.connections.adjustQuantum(allInputs)
256  return Quantum(
257  taskName=self.task.taskDef.taskName,
258  taskClass=self.task.taskDef.taskClass,
259  dataId=self.dataId,
260  initInputs=self.task.initInputs.unpackSingleRefs(),
261  inputs=allInputs,
262  outputs=self.outputs.unpackMultiRefs(),
263  )
264 
265 
266 @dataclass
268  """Helper class aggregating information about a `PipelineTask`, used when
269  constructing a `QuantumGraph`.
270 
271  See `_PipelineScaffolding` for a top-down description of the full
272  scaffolding data structure.
273 
274  Parameters
275  ----------
276  taskDef : `TaskDef`
277  Data structure that identifies the task class and its config.
278  parent : `_PipelineScaffolding`
279  The parent data structure that will hold the instance being
280  constructed.
281  datasetTypes : `TaskDatasetTypes`
282  Data structure that categorizes the dataset types used by this task.
283  """
284  def __init__(self, taskDef: TaskDef, parent: _PipelineScaffolding, datasetTypes: TaskDatasetTypes):
285  universe = parent.dimensions.universe
286  self.taskDef = taskDef
287  self.dimensions = DimensionGraph(universe, names=taskDef.connections.dimensions)
288  assert self.dimensions.issubset(parent.dimensions)
289  # Initialize _DatasetDicts as subsets of the one or two
290  # corresponding dicts in the parent _PipelineScaffolding.
291  self.initInputs = _DatasetDict.fromSubset(datasetTypes.initInputs, parent.initInputs,
292  parent.initIntermediates)
293  self.initOutputs = _DatasetDict.fromSubset(datasetTypes.initOutputs, parent.initIntermediates,
294  parent.initOutputs)
295  self.inputs = _DatasetDict.fromSubset(datasetTypes.inputs, parent.inputs, parent.intermediates)
296  self.outputs = _DatasetDict.fromSubset(datasetTypes.outputs, parent.intermediates, parent.outputs)
297  self.prerequisites = _DatasetDict.fromSubset(datasetTypes.prerequisites, parent.prerequisites)
298  self.dataIds = set()
299  self.quanta = {}
300 
301  def __repr__(self):
302  # Default dataclass-injected __repr__ gets caught in an infinite loop
303  # because of back-references.
304  return f"_TaskScaffolding(taskDef={self.taskDef}, ...)"
305 
306  taskDef: TaskDef
307  """Data structure that identifies the task class and its config
308  (`TaskDef`).
309  """
310 
311  dimensions: DimensionGraph
312  """The dimensions of a single `Quantum` of this task (`DimensionGraph`).
313  """
314 
315  initInputs: _DatasetDict
316  """Dictionary containing information about datasets used to construct this
317  task (`_DatasetDict`).
318  """
319 
320  initOutputs: _DatasetDict
321  """Dictionary containing information about datasets produced as a
322  side-effect of constructing this task (`_DatasetDict`).
323  """
324 
325  inputs: _DatasetDict
326  """Dictionary containing information about datasets used as regular,
327  graph-constraining inputs to this task (`_DatasetDict`).
328  """
329 
330  outputs: _DatasetDict
331  """Dictionary containing information about datasets produced by this task
332  (`_DatasetDict`).
333  """
334 
335  prerequisites: _DatasetDict
336  """Dictionary containing information about input datasets that must be
337  present in the repository before any Pipeline containing this task is run
338  (`_DatasetDict`).
339  """
340 
341  quanta: Dict[DataCoordinate, _QuantumScaffolding]
342  """Dictionary mapping data ID to a scaffolding object for the Quantum of
343  this task with that data ID.
344  """
345 
346  def makeQuantumSet(self) -> Set[Quantum]:
347  """Create a `set` of `Quantum` from the information in ``self``.
348 
349  Returns
350  -------
351  nodes : `set` of `Quantum
352  The `Quantum` elements corresponding to this task.
353  """
354  return set(q.makeQuantum() for q in self.quanta.values())
355 
356 
357 @dataclass
359  """A helper data structure that organizes the information involved in
360  constructing a `QuantumGraph` for a `Pipeline`.
361 
362  Parameters
363  ----------
364  pipeline : `Pipeline`
365  Sequence of tasks from which a graph is to be constructed. Must
366  have nested task classes already imported.
367  universe : `DimensionUniverse`
368  Universe of all possible dimensions.
369 
370  Notes
371  -----
372  The scaffolding data structure contains nested data structures for both
373  tasks (`_TaskScaffolding`) and datasets (`_DatasetDict`). The dataset
374  data structures are shared between the pipeline-level structure (which
375  aggregates all datasets and categorizes them from the perspective of the
376  complete pipeline) and the individual tasks that use them as inputs and
377  outputs.
378 
379  `QuantumGraph` construction proceeds in four steps, with each corresponding
380  to a different `_PipelineScaffolding` method:
381 
382  1. When `_PipelineScaffolding` is constructed, we extract and categorize
383  the DatasetTypes used by the pipeline (delegating to
384  `PipelineDatasetTypes.fromPipeline`), then use these to construct the
385  nested `_TaskScaffolding` and `_DatasetDict` objects.
386 
387  2. In `connectDataIds`, we construct and run the "Big Join Query", which
388  returns related tuples of all dimensions used to identify any regular
389  input, output, and intermediate datasets (not prerequisites). We then
390  iterate over these tuples of related dimensions, identifying the subsets
391  that correspond to distinct data IDs for each task and dataset type,
392  and then create `_QuantumScaffolding` objects.
393 
394  3. In `resolveDatasetRefs`, we run follow-up queries against all of the
395  dataset data IDs previously identified, transforming unresolved
396  DatasetRefs into resolved DatasetRefs where appropriate. We then look
397  up prerequisite datasets for all quanta.
398 
399  4. In `makeQuantumGraph`, we construct a `QuantumGraph` from the lists of
400  per-task `_QuantumScaffolding` objects.
401  """
402  def __init__(self, pipeline, *, registry):
403  _LOG.debug("Initializing data structures for QuantumGraph generation.")
404  self.tasks = []
405  # Aggregate and categorize the DatasetTypes in the Pipeline.
406  datasetTypes = PipelineDatasetTypes.fromPipeline(pipeline, registry=registry)
407  # Construct dictionaries that map those DatasetTypes to structures
408  # that will (later) hold addiitonal information about them.
409  for attr in ("initInputs", "initIntermediates", "initOutputs",
410  "inputs", "intermediates", "outputs", "prerequisites"):
411  setattr(self, attr, _DatasetDict.fromDatasetTypes(getattr(datasetTypes, attr),
412  universe=registry.dimensions))
413  # Aggregate all dimensions for all non-init, non-prerequisite
414  # DatasetTypes. These are the ones we'll include in the big join
415  # query.
416  self.dimensions = self.inputs.dimensions.union(self.intermediates.dimensions,
417  self.outputs.dimensions)
418  # Construct scaffolding nodes for each Task, and add backreferences
419  # to the Task from each DatasetScaffolding node.
420  # Note that there's only one scaffolding node for each DatasetType,
421  # shared by _PipelineScaffolding and all _TaskScaffoldings that
422  # reference it.
423  if isinstance(pipeline, Pipeline):
424  pipeline = pipeline.toExpandedPipeline()
425  self.tasks = [_TaskScaffolding(taskDef=taskDef, parent=self, datasetTypes=taskDatasetTypes)
426  for taskDef, taskDatasetTypes in zip(pipeline,
427  datasetTypes.byTask.values())]
428 
429  def __repr__(self):
430  # Default dataclass-injected __repr__ gets caught in an infinite loop
431  # because of back-references.
432  return f"_PipelineScaffolding(tasks={self.tasks}, ...)"
433 
434  tasks: List[_TaskScaffolding]
435  """Scaffolding data structures for each task in the pipeline
436  (`list` of `_TaskScaffolding`).
437  """
438 
439  initInputs: _DatasetDict
440  """Datasets consumed but not produced when constructing the tasks in this
441  pipeline (`_DatasetDict`).
442  """
443 
444  initIntermediates: _DatasetDict
445  """Datasets that are both consumed and produced when constructing the tasks
446  in this pipeline (`_DatasetDict`).
447  """
448 
449  initOutputs: _DatasetDict
450  """Datasets produced but not consumed when constructing the tasks in this
451  pipeline (`_DatasetDict`).
452  """
453 
454  inputs: _DatasetDict
455  """Datasets that are consumed but not produced when running this pipeline
456  (`_DatasetDict`).
457  """
458 
459  intermediates: _DatasetDict
460  """Datasets that are both produced and consumed when running this pipeline
461  (`_DatasetDict`).
462  """
463 
464  outputs: _DatasetDict
465  """Datasets produced but not consumed when when running this pipeline
466  (`_DatasetDict`).
467  """
468 
469  prerequisites: _DatasetDict
470  """Datasets that are consumed when running this pipeline and looked up
471  per-Quantum when generating the graph (`_DatasetDict`).
472  """
473 
474  dimensions: DimensionGraph
475  """All dimensions used by any regular input, intermediate, or output
476  (not prerequisite) dataset; the set of dimension used in the "Big Join
477  Query" (`DimensionGraph`).
478 
479  This is required to be a superset of all task quantum dimensions.
480  """
481 
482  @contextmanager
483  def connectDataIds(self, registry, collections, userQuery):
484  """Query for the data IDs that connect nodes in the `QuantumGraph`.
485 
486  This method populates `_TaskScaffolding.dataIds` and
487  `_DatasetScaffolding.dataIds` (except for those in `prerequisites`).
488 
489  Parameters
490  ----------
491  registry : `lsst.daf.butler.Registry`
492  Registry for the data repository; used for all data ID queries.
493  collections : `lsst.daf.butler.CollectionSearch`
494  Object representing the collections to search for input datasets.
495  userQuery : `str`, optional
496  User-provided expression to limit the data IDs processed.
497 
498  Returns
499  -------
500  commonDataIds : \
501  `lsst.daf.butler.registry.queries.DataCoordinateQueryResults`
502  An interface to a database temporary table containing all data IDs
503  that will appear in this `QuantumGraph`. Returned inside a
504  context manager, which will drop the temporary table at the end of
505  the `with` block in which this method is called.
506  """
507  _LOG.debug("Building query for data IDs.")
508  # Initialization datasets always have empty data IDs.
509  emptyDataId = DataCoordinate.makeEmpty(registry.dimensions)
510  for datasetType, refs in itertools.chain(self.initInputs.items(),
511  self.initIntermediates.items(),
512  self.initOutputs.items()):
513  refs[emptyDataId] = DatasetRef(datasetType, emptyDataId)
514  # Run one big query for the data IDs for task dimensions and regular
515  # inputs and outputs. We limit the query to only dimensions that are
516  # associated with the input dataset types, but don't (yet) try to
517  # obtain the dataset_ids for those inputs.
518  _LOG.debug("Submitting data ID query and materializing results.")
519  with registry.queryDataIds(self.dimensions,
520  datasets=list(self.inputs),
521  collections=collections,
522  where=userQuery,
523  ).materialize() as commonDataIds:
524  _LOG.debug("Expanding data IDs.")
525  commonDataIds = commonDataIds.expanded()
526  _LOG.debug("Iterating over query results to associate quanta with datasets.")
527  # Iterate over query results, populating data IDs for datasets and
528  # quanta and then connecting them to each other.
529  n = 0
530  for n, commonDataId in enumerate(commonDataIds):
531  # Create DatasetRefs for all DatasetTypes from this result row,
532  # noting that we might have created some already.
533  # We remember both those that already existed and those that we
534  # create now.
535  refsForRow = {}
536  for datasetType, refs in itertools.chain(self.inputs.items(), self.intermediates.items(),
537  self.outputs.items()):
538  datasetDataId = commonDataId.subset(datasetType.dimensions)
539  ref = refs.get(datasetDataId)
540  if ref is None:
541  ref = DatasetRef(datasetType, datasetDataId)
542  refs[datasetDataId] = ref
543  refsForRow[datasetType.name] = ref
544  # Create _QuantumScaffolding objects for all tasks from this
545  # result row, noting that we might have created some already.
546  for task in self.tasks:
547  quantumDataId = commonDataId.subset(task.dimensions)
548  quantum = task.quanta.get(quantumDataId)
549  if quantum is None:
550  quantum = _QuantumScaffolding(task=task, dataId=quantumDataId)
551  task.quanta[quantumDataId] = quantum
552  # Whether this is a new quantum or an existing one, we can
553  # now associate the DatasetRefs for this row with it. The
554  # fact that a Quantum data ID and a dataset data ID both
555  # came from the same result row is what tells us they
556  # should be associated.
557  # Many of these associates will be duplicates (because
558  # another query row that differed from this one only in
559  # irrelevant dimensions already added them), and we use
560  # sets to skip.
561  for datasetType in task.inputs:
562  ref = refsForRow[datasetType.name]
563  quantum.inputs[datasetType.name][ref.dataId] = ref
564  for datasetType in task.outputs:
565  ref = refsForRow[datasetType.name]
566  quantum.outputs[datasetType.name][ref.dataId] = ref
567  _LOG.debug("Finished processing %d rows from data ID query.", n)
568  yield commonDataIds
569 
570  def resolveDatasetRefs(self, registry, collections, run, commonDataIds, *, skipExisting=True):
571  """Perform follow up queries for each dataset data ID produced in
572  `fillDataIds`.
573 
574  This method populates `_DatasetScaffolding.refs` (except for those in
575  `prerequisites`).
576 
577  Parameters
578  ----------
579  registry : `lsst.daf.butler.Registry`
580  Registry for the data repository; used for all data ID queries.
581  collections : `lsst.daf.butler.CollectionSearch`
582  Object representing the collections to search for input datasets.
583  run : `str`, optional
584  Name of the `~lsst.daf.butler.CollectionType.RUN` collection for
585  output datasets, if it already exists.
586  commonDataIds : \
587  `lsst.daf.butler.registry.queries.DataCoordinateQueryResults`
588  Result of a previous call to `connectDataIds`.
589  skipExisting : `bool`, optional
590  If `True` (default), a Quantum is not created if all its outputs
591  already exist in ``run``. Ignored if ``run`` is `None`.
592 
593  Raises
594  ------
595  OutputExistsError
596  Raised if an output dataset already exists in the output run
597  and ``skipExisting`` is `False`. The case where some but not all
598  of a quantum's outputs are present and ``skipExisting`` is `True`
599  cannot be identified at this stage, and is handled by `fillQuanta`
600  instead.
601  """
602  # Look up [init] intermediate and output datasets in the output
603  # collection, if there is an output collection.
604  if run is not None:
605  for datasetType, refs in itertools.chain(self.initIntermediates.items(),
606  self.initOutputs.items(),
607  self.intermediates.items(),
608  self.outputs.items()):
609  _LOG.debug("Resolving %d datasets for intermediate and/or output dataset %s.",
610  len(refs), datasetType.name)
611  isInit = datasetType in self.initIntermediates or datasetType in self.initOutputs
612  resolvedRefQueryResults = commonDataIds.subset(
613  datasetType.dimensions,
614  unique=True
615  ).findDatasets(
616  datasetType,
617  collections=run,
618  findFirst=True
619  )
620  for resolvedRef in resolvedRefQueryResults:
621  # TODO: we could easily support per-DatasetType
622  # skipExisting and I could imagine that being useful - it's
623  # probably required in order to support writing initOutputs
624  # before QuantumGraph generation.
625  assert resolvedRef.dataId in refs
626  if skipExisting or isInit:
627  refs[resolvedRef.dataId] = resolvedRef
628  else:
629  raise OutputExistsError(f"Output dataset {datasetType.name} already exists in "
630  f"output RUN collection '{run}' with data ID"
631  f" {resolvedRef.dataId}.")
632  # Look up input and initInput datasets in the input collection(s).
633  for datasetType, refs in itertools.chain(self.initInputs.items(), self.inputs.items()):
634  _LOG.debug("Resolving %d datasets for input dataset %s.", len(refs), datasetType.name)
635  resolvedRefQueryResults = commonDataIds.subset(
636  datasetType.dimensions,
637  unique=True
638  ).findDatasets(
639  datasetType,
640  collections=collections,
641  findFirst=True
642  )
643  dataIdsNotFoundYet = set(refs.keys())
644  for resolvedRef in resolvedRefQueryResults:
645  dataIdsNotFoundYet.discard(resolvedRef.dataId)
646  refs[resolvedRef.dataId] = resolvedRef
647  if dataIdsNotFoundYet:
648  raise RuntimeError(
649  f"{len(dataIdsNotFoundYet)} dataset(s) of type "
650  f"'{datasetType.name}' was/were present in a previous "
651  f"query, but could not be found now."
652  f"This is either a logic bug in QuantumGraph generation "
653  f"or the input collections have been modified since "
654  f"QuantumGraph generation began."
655  )
656  # Copy the resolved DatasetRefs to the _QuantumScaffolding objects,
657  # replacing the unresolved refs there, and then look up prerequisites.
658  for task in self.tasks:
659  _LOG.debug(
660  "Applying resolutions and finding prerequisites for %d quanta of task with label '%s'.",
661  len(task.quanta),
662  task.taskDef.label
663  )
664  lookupFunctions = {
665  c.name: c.lookupFunction
666  for c in iterConnections(task.taskDef.connections, "prerequisiteInputs")
667  if c.lookupFunction is not None
668  }
669  dataIdsToSkip = []
670  for quantum in task.quanta.values():
671  # Process outputs datasets only if there is a run to look for
672  # outputs in and skipExisting is True. Note that if
673  # skipExisting is False, any output datasets that already exist
674  # would have already caused an exception to be raised.
675  # We never update the DatasetRefs in the quantum because those
676  # should never be resolved.
677  if run is not None and skipExisting:
678  resolvedRefs = []
679  unresolvedRefs = []
680  for datasetType, originalRefs in quantum.outputs.items():
681  for ref in task.outputs.extract(datasetType, originalRefs.keys()):
682  if ref.id is not None:
683  resolvedRefs.append(ref)
684  else:
685  unresolvedRefs.append(ref)
686  if resolvedRefs:
687  if unresolvedRefs:
688  raise OutputExistsError(
689  f"Quantum {quantum.dataId} of task with label "
690  f"'{quantum.task.taskDef.label}' has some outputs that exist "
691  f"({resolvedRefs}) "
692  f"and others that don't ({unresolvedRefs})."
693  )
694  else:
695  # All outputs are already present; skip this
696  # quantum and continue to the next.
697  dataIdsToSkip.append(quantum.dataId)
698  continue
699  # Update the input DatasetRefs to the resolved ones we already
700  # searched for.
701  for datasetType, refs in quantum.inputs.items():
702  for ref in task.inputs.extract(datasetType, refs.keys()):
703  refs[ref.dataId] = ref
704  # Look up prerequisite datasets in the input collection(s).
705  # These may have dimensions that extend beyond those we queried
706  # for originally, because we want to permit those data ID
707  # values to differ across quanta and dataset types.
708  for datasetType in task.prerequisites:
709  lookupFunction = lookupFunctions.get(datasetType.name)
710  if lookupFunction is not None:
711  # PipelineTask has provided its own function to do the
712  # lookup. This always takes precedence.
713  refs = list(
714  lookupFunction(datasetType, registry, quantum.dataId, collections)
715  )
716  elif (datasetType.isCalibration()
717  and datasetType.dimensions <= quantum.dataId.graph
718  and quantum.dataId.graph.temporal):
719  # This is a master calibration lookup, which we have to
720  # handle specially because the query system can't do a
721  # temporal join on a non-dimension-based timespan yet.
722  timespan = quantum.dataId.timespan
723  try:
724  refs = [registry.findDataset(datasetType, quantum.dataId,
725  collections=collections,
726  timespan=timespan)]
727  except KeyError:
728  # This dataset type is not present in the registry,
729  # which just means there are no datasets here.
730  refs = []
731  else:
732  # Most general case.
733  refs = list(registry.queryDatasets(datasetType,
734  collections=collections,
735  dataId=quantum.dataId,
736  findFirst=True).expanded())
737  quantum.prerequisites[datasetType].update({ref.dataId: ref for ref in refs
738  if ref is not None})
739  # Actually remove any quanta that we decided to skip above.
740  if dataIdsToSkip:
741  _LOG.debug("Pruning %d quanta for task with label '%s' because all of their outputs exist.",
742  len(dataIdsToSkip), task.taskDef.label)
743  for dataId in dataIdsToSkip:
744  del task.quanta[dataId]
745 
746  def makeQuantumGraph(self):
747  """Create a `QuantumGraph` from the quanta already present in
748  the scaffolding data structure.
749 
750  Returns
751  -------
752  graph : `QuantumGraph`
753  The full `QuantumGraph`.
754  """
755  graph = QuantumGraph({task.taskDef: task.makeQuantumSet() for task in self.tasks})
756  return graph
757 
758 
759 class _InstrumentFinder(TreeVisitor):
760  """Implementation of TreeVisitor which looks for instrument name
761 
762  Instrument should be specified as a boolean expression
763 
764  instrument = 'string'
765  'string' = instrument
766 
767  so we only need to find a binary operator where operator is "=",
768  one side is a string literal and other side is an identifier.
769  All visit methods return tuple of (type, value), non-useful nodes
770  return None for both type and value.
771  """
772  def __init__(self):
773  self.instruments = []
774 
775  def visitNumericLiteral(self, value, node):
776  # do not care about numbers
777  return (None, None)
778 
779  def visitStringLiteral(self, value, node):
780  # return type and value
781  return ("str", value)
782 
783  def visitTimeLiteral(self, value, node):
784  # do not care about these
785  return (None, None)
786 
787  def visitRangeLiteral(self, start, stop, stride, node):
788  # do not care about these
789  return (None, None)
790 
791  def visitIdentifier(self, name, node):
792  if name.lower() == "instrument":
793  return ("id", "instrument")
794  return (None, None)
795 
796  def visitUnaryOp(self, operator, operand, node):
797  # do not care about these
798  return (None, None)
799 
800  def visitBinaryOp(self, operator, lhs, rhs, node):
801  if operator == "=":
802  if lhs == ("id", "instrument") and rhs[0] == "str":
803  self.instruments.append(rhs[1])
804  elif rhs == ("id", "instrument") and lhs[0] == "str":
805  self.instruments.append(lhs[1])
806  return (None, None)
807 
808  def visitIsIn(self, lhs, values, not_in, node):
809  # do not care about these
810  return (None, None)
811 
812  def visitParens(self, expression, node):
813  # do not care about these
814  return (None, None)
815 
816 
817 def _findInstruments(queryStr):
818  """Get the names of any instrument named in the query string by searching
819  for "instrument = <value>" and similar patterns.
820 
821  Parameters
822  ----------
823  queryStr : `str` or None
824  The query string to search, or None if there is no query.
825 
826  Returns
827  -------
828  instruments : `list` [`str`]
829  The list of instrument names found in the query.
830 
831  Raises
832  ------
833  ValueError
834  If the query expression can not be parsed.
835  """
836  if not queryStr:
837  return []
838  parser = ParserYacc()
839  finder = _InstrumentFinder()
840  try:
841  tree = parser.parse(queryStr)
842  except ParseError as exc:
843  raise ValueError(f"failed to parse query expression: {queryStr}") from exc
844  tree.visit(finder)
845  return finder.instruments
846 
847 
848 # ------------------------
849 # Exported definitions --
850 # ------------------------
851 
852 
853 class GraphBuilderError(Exception):
854  """Base class for exceptions generated by graph builder.
855  """
856  pass
857 
858 
859 class OutputExistsError(GraphBuilderError):
860  """Exception generated when output datasets already exist.
861  """
862  pass
863 
864 
866  """Exception generated when a prerequisite dataset does not exist.
867  """
868  pass
869 
870 
871 class GraphBuilder(object):
872  """GraphBuilder class is responsible for building task execution graph from
873  a Pipeline.
874 
875  Parameters
876  ----------
877  registry : `~lsst.daf.butler.Registry`
878  Data butler instance.
879  skipExisting : `bool`, optional
880  If `True` (default), a Quantum is not created if all its outputs
881  already exist.
882  """
883 
884  def __init__(self, registry, skipExisting=True):
885  self.registry = registry
886  self.dimensions = registry.dimensions
887  self.skipExisting = skipExisting
888 
889  def makeGraph(self, pipeline, collections, run, userQuery):
890  """Create execution graph for a pipeline.
891 
892  Parameters
893  ----------
894  pipeline : `Pipeline`
895  Pipeline definition, task names/classes and their configs.
896  collections : `lsst.daf.butler.CollectionSearch`
897  Object representing the collections to search for input datasets.
898  run : `str`, optional
899  Name of the `~lsst.daf.butler.CollectionType.RUN` collection for
900  output datasets, if it already exists.
901  userQuery : `str`
902  String which defines user-defined selection for registry, should be
903  empty or `None` if there is no restrictions on data selection.
904 
905  Returns
906  -------
907  graph : `QuantumGraph`
908 
909  Raises
910  ------
911  UserExpressionError
912  Raised when user expression cannot be parsed.
913  OutputExistsError
914  Raised when output datasets already exist.
915  Exception
916  Other exceptions types may be raised by underlying registry
917  classes.
918  """
919  scaffolding = _PipelineScaffolding(pipeline, registry=self.registry)
920 
921  instrument = pipeline.getInstrument()
922  if isinstance(instrument, str):
923  instrument = doImport(instrument)
924  instrumentName = instrument.getName() if instrument else None
925  userQuery = self._verifyInstrumentRestriction(instrumentName, userQuery)
926 
927  with scaffolding.connectDataIds(self.registry, collections, userQuery) as commonDataIds:
928  scaffolding.resolveDatasetRefs(self.registry, collections, run, commonDataIds,
929  skipExisting=self.skipExisting)
930  return scaffolding.makeQuantumGraph()
931 
932  @staticmethod
933  def _verifyInstrumentRestriction(instrumentName, query):
934  """Add an instrument restriction to the query if it does not have one,
935  and verify that if given an instrument name that there are no other
936  instrument restrictions in the query.
937 
938  Parameters
939  ----------
940  instrumentName : `str`
941  The name of the instrument that should appear in the query.
942  query : `str`
943  The query string.
944 
945  Returns
946  -------
947  query : `str`
948  The query string with the instrument added to it if needed.
949 
950  Raises
951  ------
952  RuntimeError
953  If the pipeline names an instrument and the query contains more
954  than one instrument or the name of the instrument in the query does
955  not match the instrument named by the pipeline.
956  """
957  if not instrumentName:
958  return query
959  queryInstruments = _findInstruments(query)
960  if len(queryInstruments) > 1:
961  raise RuntimeError(f"When the pipeline has an instrument (\"{instrumentName}\") the query must "
962  "have zero instruments or one instrument that matches the pipeline. "
963  f"Found these instruments in the query: {queryInstruments}.")
964  if not queryInstruments:
965  # There is not an instrument in the query, add it:
966  restriction = f"instrument = '{instrumentName}'"
967  _LOG.debug(f"Adding restriction \"{restriction}\" to query.")
968  query = f"{restriction} AND ({query})" if query else restriction # (there may not be a query)
969  elif queryInstruments[0] != instrumentName:
970  # Since there is an instrument in the query, it should match
971  # the instrument in the pipeline.
972  raise RuntimeError(f"The instrument named in the query (\"{queryInstruments[0]}\") does not "
973  f"match the instrument named by the pipeline (\"{instrumentName}\")")
974  return query
lsst::pipe::base.graphBuilder._PipelineScaffolding.dimensions
dimensions
Definition: graphBuilder.py:416
lsst::pipe::base.graphBuilder._PipelineScaffolding.tasks
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Definition: graphBuilder.py:404
lsst::pipe::base.graphBuilder._QuantumScaffolding.__init__
def __init__(self, _TaskScaffolding task, DataCoordinate dataId)
Definition: graphBuilder.py:201
lsst::pipe::base.graphBuilder._QuantumScaffolding.prerequisites
prerequisites
Definition: graphBuilder.py:206
lsst::pipe::base.graphBuilder._TaskScaffolding.prerequisites
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Definition: graphBuilder.py:297
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_DatasetDict fromSubset(cls, Iterable[DatasetType] datasetTypes, _DatasetDict first, *_DatasetDict rest)
Definition: graphBuilder.py:100
lsst::pipe::base.graphBuilder.GraphBuilder.skipExisting
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Definition: graphBuilder.py:887
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def visitStringLiteral(self, value, node)
Definition: graphBuilder.py:779
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Definition: graphBuilder.py:483
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Definition: graphBuilder.py:301
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Definition: graphBuilder.py:286
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Definition: graphBuilder.py:211
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Definition: graphBuilder.py:296
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Definition: graphBuilder.py:346
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Definition: graphBuilder.py:291
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Definition: graphBuilder.py:429
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Definition: graphBuilder.py:205
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Definition: graphBuilder.py:203
lsst::pipe::base.graphBuilder._InstrumentFinder.visitTimeLiteral
def visitTimeLiteral(self, value, node)
Definition: graphBuilder.py:783
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def _verifyInstrumentRestriction(instrumentName, query)
Definition: graphBuilder.py:933
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Definition: graph.py:54
lsst::pipe::base.graphBuilder._QuantumScaffolding.makeQuantum
Quantum makeQuantum(self)
Definition: graphBuilder.py:241
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_DatasetDict fromDatasetTypes(cls, Iterable[DatasetType] datasetTypes, *DimensionUniverse universe)
Definition: graphBuilder.py:80
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Definition: graphBuilder.py:865
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Definition: graphBuilder.py:64
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Definition: graphBuilder.py:204
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Definition: graphBuilder.py:202
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Definition: connections.py:501
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Definition: graphBuilder.py:293
lsst::pipe::base.graphBuilder._PipelineScaffolding.__init__
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Definition: graphBuilder.py:402
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Definition: graphBuilder.py:775
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Definition: graphBuilder.py:295
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Definition: graphBuilder.py:570
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Definition: graphBuilder.py:796
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Definition: graphBuilder.py:800
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Definition: graphBuilder.py:358
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Definition: graphBuilder.py:164
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Definition: graphBuilder.py:75
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Definition: graphBuilder.py:759
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lsst::pipe::base.graphBuilder._InstrumentFinder.__init__
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Definition: graphBuilder.py:772
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Definition: graphBuilder.py:298
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Definition: graphBuilder.py:885
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Definition: graphBuilder.py:299
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Definition: graphBuilder.py:889
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Definition: graphBuilder.py:152
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